LAB 2 - Visualization of Packages

Importing of plotly

import plotly
plotly.offline.init_notebook_mode()

Graph Creation From Different Tools

Matplotlib

I. bar(x, height)

import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# make data:
x = 0.5 + np.arange(8)
y = [4.8, 5.5, 3.5, 4.6, 6.5, 6.6, 2.6, 3.0]

# plot
fig, ax = plt.subplots()

ax.bar(x, y, width=1, edgecolor="white", linewidth=0.7)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
       ylim=(0, 8), yticks=np.arange(1, 8))

plt.show()

This plot comes under Pairwise data which works by each alternative being compared against every other alternative in pairs.

II. pie(x)

import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery-nogrid')


# make data
x = [1, 2, 3, 4]
colors = plt.get_cmap('Blues')(np.linspace(0.2, 0.7, len(x)))

# plot
fig, ax = plt.subplots()
ax.pie(x, colors=colors, radius=3, center=(4, 4),
       wedgeprops={"linewidth": 1, "edgecolor": "white"}, frame=True)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
       ylim=(0, 8), yticks=np.arange(1, 8))

plt.show()

This plot comes under Statistical Distributions. Plots of the distribution of at least one variable in a dataset. Some of these methods also compute the distributions.

Seaborn Tool

I. Timeseries plot with error bands

import seaborn as sns
sns.set_theme(style="darkgrid")

# Load an example dataset with long-form data
fmri = sns.load_dataset("fmri")

# Plot the responses for different events and regions
sns.lineplot(x="timepoint", y="signal",
             hue="region", style="event",
             data=fmri)

<Axes: xlabel='timepoint', ylabel='signal'>

The resulting plot displays how the "signal" variable changes over "timepoint" for different "events" and "regions."

II. FacetGrid - Facetting histograms by subsets of data

import seaborn as sns

sns.set_theme(style="darkgrid")
df = sns.load_dataset("penguins")
sns.displot(
    df, x="flipper_length_mm", col="species", row="sex",
    binwidth=3, height=3, facet_kws=dict(margin_titles=True),
)

<seaborn.axisgrid.FacetGrid at 0x21b3434cc90>

Each subplot represents the distribution of "flipper_length_mm" for a specific combination of penguin species and sex. The x-axis represents "flipper_length_mm," and the y-axis represents the frequency of occurrences within each bin.

Plotly Express

I. Scatter Plots and Discrete Colour

import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species")
fig.show()

To create a scatter plot of the Iris dataset, visualizing the relationship between sepal width and sepal length, with data points colored by the species of the iris flowers.

II. Barcharts

import plotly.express as px
df = px.data.tips()
fig = px.bar(df, x="sex", y="total_bill", color="smoker", barmode="group")
fig.show()

The chart depicts the total bill amounts for different genders ("sex") in two separate groups based on smoking status ("smoker"), providing a visual comparison of total bills among different categories.

Additional Markdown Elements

I. Incorporating a hyperlink in Markdown

Click here to visit Google

II .Incorporating a table in Markdown

Stretch/Untouched	ProbDistribution	Accuracy
Stretched	Gaussian	.843

III. Incorporating an image in Markdown

from PIL import Image
from matplotlib import pyplot as plt

img = Image.open('C:/CSCN8010-Lab/Desk/tree.jpg')
fig = plt.imshow(img)
plt.axis('off')
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)

IV. Incorporating a Dynamic JS-based Graph

px.scatter(x=[1, 2, 3], y=[1, 0, 3])